1,545 research outputs found

    Battery Electric Storage Systems: Advances, Challenges, and Market Trends

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    The increasing integration of renewable energy sources (RESs) and the growing demand for sustainable power solutions have necessitated the widespread deployment of energy storage systems. Among these systems, battery energy storage systems (BESSs) have emerged as a promising technology due to their flexibility, scalability, and cost-effectiveness. This paper aims to provide a comprehensive review of the diffusion and deployment of BESSs across various applications, analyzing their impact on grid stability, renewable energy integration, and the overall energy transition. The paper examines the key drivers and challenges associated with BESS adoption, as well as market trends influencing their proliferation. Through an analysis of empirical data, this study aims to shed light on the current state of BESS diffusion. Finally, this research contributes to the knowledge base surrounding battery storage technology and provides insights into its role in achieving a sustainable and reliable energy future

    Modular AC coupled hybrid power systems for the emerging GHG mitigation products market

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    Bioenergy systems particularly waste to energy (WTE) systems are increasingly gaining prominence. Market for modular hybrid energy systems (HES) combining renewable energy sources including WTEs is potentially large. Novel configuration of AC coupling for HES is discussed. Emerging opportunities for market development of hybrid energy systems under green house gas mitigation initiatives particularly Kyoto flexibility mechanisms is analysed

    Mission-Profile-Based System-Level Reliability Analysis in DC Microgrids

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    Rooftop PV and the renewable energy transition : a review of driving forces and analytical frameworks

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    Rooftop solar photovoltaics (PV) are accelerating the transition towards low carbon electricity systems in many countries, particularly in Australia. This review paper provides an overview of the (1) technical, (2) economic, (3) socio-political, and (4) regulatory and institutional aspects that should be considered concurrently when navigating the transition towards a rooftop PV-dominated electricity system. We consider the suitability of two prominent long-range transitions theories for understanding the importance and interaction of elements within these four aspects during the transition. The multi-level perspective (MLP) of transitions theory is considered best suited for this task as it addresses fundamental shifts in the socio-technical systems, rather than being weighted towards technological and/or economic solutions. We find that relatively little research has been undertaken where the renewable energy transition is being driven by the uptake of rooftop PV within the distribution network of established islanded electricity systems. These islanded electricity systems will be the first to experience system impacts from high levels of rooftop PV. This review provides further analysis of important gaps in understanding the rooftop-PV-led energy transition and the implications for policy makers in maintaining stable electricity supplies during the transition

    Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model

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    Battery energy storage systems (BESS) play a pivotal role in future power systems as they contribute to achiev-ing the net-zero carbon emission objectives. The BESS systems, predominantly employing lithium-ion batteries, have been exten-sively deployed. The degradation of these batteries significantly affects system efficiency. Deep neural networks can accurately quantify the battery degradation, however, the model complexity hinders their applications in energy scheduling for various power systems at different scales. To address this issue, this paper pre-sents a novel approach, introducing a linearized sparse neural network-based battery degradation model (SNNBD), specifically tailored to quantify battery degradation based on the scheduled BESS daily operational profiles. By leveraging sparse neural networks, this approach achieves accurate degradation predic-tion while substantially reducing the complexity associated with a dense neural network model. The computational burden of inte-grating battery degradation into day-ahead energy scheduling is thus substantially alleviated. Case studies, conducted on both microgrids and bulk power grids, demonstrated the efficiency and suitability of the proposed SNNBD-integrated scheduling model that can effectively address battery degradation concerns while optimizing day-ahead energy scheduling operations

    Techno-Economic modelling of hybrid renewable mini-grids for rural electrification planning in Sub-Saharan Africa

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    Access to clean, modern energy services is a necessity for sustainable development. The UN Sustainable Development Goals and SE4ALL program commit to the provision of universal access to modern energy services by 2030. However, the latest available figures estimate that 1.1 billion people are living without access to electricity, with over 55% living in Sub-Saharan Africa. Furthermore, 85% live in rural areas, often with challenging terrain, low income and population density; or in countries with severe underinvestment in electricity infrastructure making grid extension unrealistic. Recently, improvements in technology, cost efficiency and new business models have made mini-grids which combine multiple energy technologies in hybrid systems one of the most promising alternatives for electrification off the grid. The International Energy Agency has estimated that up to 350,000 new mini-grids will be required to reach universal access goals by 2030. Given the intermittent and location-dependent nature of renewable energy sources, the evolving costs and performance characteristics of individual technologies, and the characteristics of interacting technologies, detailed system simulation and demand modelling is required to determine the cost optimal combinations of technologies for each-and-every potential mini-grid site. Adding to this are the practical details on the ground such as community electricity demand profiles and distances to the grid or fuel sources, as well asthe social and political contexts,such as unknown energy demand uptake or technology acceptance, national electricity system expansion plans and subsidies or taxes, among others. These can all have significant impacts in deciding the applicability of a mini-grid within that context. The scope of the research and modelling framework presented focuses primarily on meeting the specific energy needs in the sub-Saharan African context. Thus, in being transparent, utilizing freely available software and data as well as aiming to be reproducible, scalable and customizable; the model aims to be fully flexible, staying relevant to other unique contexts and useful in answering unknown future research questions. The techno-economic model implementation presented in this paper simulates hourly mini-grid operation using meteorological data, demand profiles, technology capabilities, and costing data to determine the optimal component sizing of hybrid mini-grids appropriate for rural electrification. The results demonstrate the location, renewable resource, technology cost and performance dependencies on system sizing. The model is applied for the investigation of 15 hypothetical mini-grids sites in different regions of South Africa to validate and demonstrate the model’s capabilities. The effect of technology hybridization and future technology cost reductions on the expected cost of energy and the optimal technology configurations are demonstrated. The modelling results also showed that the combination of hydrogen fuel cell and electrolysers was not an economical energy storage with present day technology costs and performance. Thereafter, the model was used to determine an approximate fuel cell and electrolyser cost target curve up to the year 2030. Ultimately, any research efforts through the application of the model, building on the presented framework, are intended to bridge the science-policy boundary and give credible insight for energy and electrification policies, as well as identifying high impact focus areas for ongoing further research

    Reliability Analysis of Fast Electric Vehicle Charging Systems

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